Investigation on the performance of meta-heuristics for solving single objective conceptual design of a conventional fixed wing unmanned aerial vehicle

Main Article Content

P. Champasak
N. Panagant
S. Bureerat
N. Pholdee

Abstract

In this work, conceptual design optimisation of a conventional fixed wing unmanned aerial vehicle (UAV) is performed through metaheuristics. Five optimisation objective functions including take-off gross weight , take off distance , endurance , lift coefficient  and drag coefficient at cruising  are scalarised into a single-objective optimisation problem subject to aircraft flight mission, performance, and stability constraints. Aerodynamic and stability analyses are executed by a vortex lattice method (VLM) while aircraft component weights and aircraft performance are estimated by empirical equations. Six state-of-the-art of single-objective meta-heuristics (MH) including Equilibrium Optimizer (EO), Evolution Strategies algorithm (ES), Moth-Flame Optimization Algorithm (MFO), Marine Predators Algorithm (MPA), Slime Mould Algorithm (SMA), and Salp Swarm Algorithm (SSA) are employed to solve the problem while their search performance are statistically investigated based on the Friedman test. The results obtained shown that the best and second-best optimiser are EO and MFA, respectively. Based on this study, the optimal result which can be chosen for further design stages (preliminary and detail design) is revealed.

Downloads

Download data is not yet available.

Article Details

How to Cite
Champasak, P., Panagant, N., Bureerat, S., & Pholdee, N. (2022). Investigation on the performance of meta-heuristics for solving single objective conceptual design of a conventional fixed wing unmanned aerial vehicle. Journal of Research and Applications in Mechanical Engineering, 10(1), JRAME–22. Retrieved from https://ph01.tci-thaijo.org/index.php/jrame/article/view/246491
Section
RESEARCH ARTICLES

References

Bravo-Mosquera, P.D., Cerón-Muñoz, H.D., Díaz-Vázquez, G. and Martini Catalano, F. Conceptual design and CFD analysis of a new prototype of agricultural aircraft, Aerospace Science and Technology, Vol. 80, 2018, pp. 156-176.

Panagiotou, P., Kaparos, P., Salpingidou, C. and Yakinthos, K. Aerodynamic design of a MALE UAV, Aerospace Science and Technology, Vol. 50, 2016, pp. 127-138.

Bravo-Mosquera, P.D., Botero-Bolivar, L., Acevedo-Giraldo, D. and Cerón-Muñoz, H.D. Aerodynamic design analysis of a UAV for superficial research of volcanic environments, Aerospace Science and Technology, Vol. 70, 2017, pp. 600-614.

Budziak, K. Aerodynamic analysis with Athena Vortex Lattice (AVL), 2015, Hamburg university of applied sciences, Hamburg.

Deperrois, A. XFLR5: Analysis of foils and wings operating at low Reynolds numbers, URL: http://www.xflr5.tech/xflr5.htm, accessed on 29/09/2021.

Stanford Aerospace Design Lab, Stanford University, United States. SUAVE: Aerospace conceptual design environment, URL: https://suave.stanford.edu/, accessed on 29/09/2021.

NASA. OpenVSP: NASA open source parametric geometry, URL: https://blog.adafruit.com/2012/01/16/ openvsp-nasa-open-source-parametric-geometry/, accessed on 29/09/2021.

Oh, T.H. Conceptual design of small unmanned aerial vehicle with proton exchange membrane fuel cell system for long endurance mission, Energy Conversion and Management, Vol. 176, 2018, pp. 349-356.

Mohammad Zadeh, P. and Sayadi, M. An efficient aerodynamic shape optimization of blended wing body UAV using multi-fidelity models, Chinese Journal of Aeronautics, Vol. 31(6), 2018, pp. 1165-1180.

Singh, V., Sharma, S.K. and Vaibhav, S. Transport aircraft conceptual design optimization using real coded genetic algorithm, Vol. 2016, 2016, Article number: 2813541.

Sastry, K., Goldberg, D. and Kendall, G. Genetic algorithms, In: Burke, E.K. and Kendall, G., Eds. Search methodologies: Introductory tutorials in optimization and decision support techniques, 2005, Springer, Boston, pp. 97-125.

Deb, K., Agrawal, S., Pratap, A. and Meyarivan, T. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II BT, paper presented in the Parallel problem solving from nature PPSN VI, 2000, Paris, France.

Kennedy, J. and Eberhart, R. Particle swarm optimization, paper presented in the Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, Perth, Australia.

Storn, R. and Price, K. Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, Vol. 11, 1997, pp. 341-359.

Leifsson, L., Ko, A., Mason, W.H., Schetz, J.A., Grossman, B. and Haftka, R.T. Multidisciplinary design optimization of blended-wing-body transport aircraft with distributed propulsion, Aerospace Science and Technology, Vol. 25(1), 2013, pp. 16-28.

Wroblewski, G.E. and Ansell, P.J. Mission analysis and emissions for conventional and hybrid-electric commercial transport aircraft, Journal of Aircraft, Vol. 56(3), 2019, pp. 1200-1213.

Riboldi, C.E.D., Gualdoni, F. and Trainelli, L. Preliminary weight sizing of light pure-electric and hybrid-electric aircraft, Transportation Research Procedia, Vol. 29, 2018, pp. 376-389.

Silva, H.L., Resende, G.J., Neto, R.M.C., Carvalho, A.R.D., Gil, A.A., Cruz, M.A.A., et al. A multidisciplinary design optimization for conceptual design of hybrid-electric aircraft, Structural and Multidisciplinary Optimization, Vol. 64, 2021, pp. 3505-3526.

Panagant, N. and Bureerat, S. Truss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution, Engineering Optimization, Vol. 50(10), 2018, pp. 1645-1661.

Marques, J., Cunha, M. and Savić, D. Many-objective optimization model for the flexible design of water distribution networks, Journal of Environmental Management, Vol. 226, 2018, pp. 308-319.

Patel, V., Savsani, V. and Mudgal, A. Many-objective thermodynamic optimization of Stirling heat engine, Energy, Vol. 125, 2017, pp. 629-642.

Zhang, B., Zhu, J., Xiang, G. and Gao, L. Design of nanofluid-cooled heat sink using topology optimization, Chinese Journal of Aeronautics, Vol. 34(2), 2020, pp. 301-317.

Faramarzi, A., Heidarinejad, M., Stephens, B. and Mirjalili, S. Equilibrium optimizer: A novel optimization algorithm, Knowledge-Based Systems, Vol. 191, 2020, Article number: 105190.

Beyer, H.G. and Schwefel, H.P. Evolution strategies - A comprehensive introduction, Natural Computing, Vol. 1, 2002, pp. 3-52.

Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Systems, Vol. 89, 2015, pp. 228-249.

Faramarzi, A., Heidarinejad, M., Mirjalili, S. and Gandomi, A.H. Marine predators algorithm: A nature-inspired metaheuristic, Expert Systems with Applications, Vol. 152, 2020, Article number: 113377.

Li, S., Chen, H., Wang, M., Heidari, A.A. and Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization, Future Generation Computer Systems, Vol. 111, 2020, pp. 300-323.

Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S., Faris, H. and Mirjalili, S.M. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems, Advances in Engineering Software, Vol. 114, 2017, pp. 163-191.

Essari, A. Estimation of component design weights in conceptual design phase for tactical UAVs, Dissertation, 2015, University of Belgrade, Belgrade.

Sadraey, M. Aircraft performance analysis, 2009, VDM Verlag Dr. Müller, Saarbrücken.